A Super Resolution Generative Adversarial Network with Bidirectional Encoder Representations from Transformers for Aspect-based Sentiment Analysis
Keywords:
Aspect-based sentiment analysis (ABSA), Bidirectional Encoder Representations from Transformers (BERT), Sentiment polarity identification, Social media sentiment monitoring, Super Resolution Generative Adversarial Networks (SRGAN),Abstract
Aspect-based sentiment analysis (ABSA) determines the sentiment polarity associated with features stated in a sentence or text. Current sentiment analysis algorithms based on aspect categories frequently need to account for the implicit context of aspect-category information. Existing models may yield acceptable results but often require more topic expertise. Current sentiment analysis algorithms based on aspect categories frequently need to account for the implicit context of aspect-category information. Existing models may yield acceptable results, but they often lack topic expertise. We introduce a novel technique that employs Super Resolution Generative Adversarial Networks (SRGAN) and Bidirectional Encoder Representations from Transformers (BERT) to solve the complexities of this problem and improve sentiment analysis accuracy. We offer a synergistic framework in this study that employs SRGANs to enhance the resolution and clarity of text representations, followed by incorporating BERT's contextual embeddings for aspect-based sentiment analysis. By integrating SRGAN's ability to build high-resolution text representations with BERT's contextualized language understanding, SRGANs-BERT overcomes the issues of aspect extraction, sentiment polarity identification, and context-dependent sentiment comprehension. The combination of SRGANs with BERT suggests a path forward for aspect-based sentiment analysis, with applications including customer feedback analysis, market research, and social media sentiment monitoring. We illustrate the usefulness of our suggested strategy through rigorous testing on benchmark data sets. Our findings show that combining SRGANs and BERT significantly improves aspect-based sentiment analysis's efficacy.
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J. Li, B. Chiu, S. Shang, and L. Shao, “Neural text segmentation and its application to sentiment analysis”, IEEE Trans. Knowl. Data Eng. 34, 828–842, 2022.
C. Chen, Z. Teng, Z. Wang, and Y. Zhang, 2022. “Discrete opinion tree induction for aspect-based sentiment analysis,” in: ACL, Association for Computational Linguistics. pp. 2051–2064.
R. Li, H. Chen, F. Feng, Z. Ma, X. Wang, and E. H. Hovy, “Dual graph convolutional networks for aspect-based sentiment analysis”, in: ACL, Association for Computational Linguistics. pp. 6319–6329, 2021.
H. H. Do, P. W. Prasad, A. Maag, and A. Alsadoon, “Deep learning for aspect-based sentiment analysis: a comparative review”, Expert systems with applications 118, 272–299, 2019.
D. Li, K. Ahmed, Z. Zheng, S. A. H. Mohsan, M. H. Alsharif, M. Hadjouni, and S. M. Mostafa, “Roman Urdu Sentiment Analysis Using Transfer Learning,” Appl. Sci. 12 (20), 10344, 2022.
H. Zhao, Z. Liu, X. Yao, and Q. Yang, “A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach”, Informat. Process. Manage. 58 (5), 102656, 2021.
H. Tang, D. Ji, C. Li, and Q. Zhou, “Dependency graph enhanced dual transformer structure for aspect-based sentiment classification”, In Proceedings of the 58th Annual meeting of the association for computational linguistics, pp. 6578–6588, 2021.
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, Albert: “A lite bert for self-supervised learning of language representations”, arXiv preprint, 2019 arXiv:1909.11942.
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen et al. “Roberta: A robustly optimized bert pretraining approach,” arXiv preprint, 2019, arXiv:1907.11692.
A. Žunić, P. Corcoran, and I. Spasić, “Aspect-based sentiment analysis with graph convolution over syntactic dependencies”, Artificial Intelligence in Medicine, 119, Article 102138, 2021. http://dx.doi.org/10.1016/j.artmed.2021.102138
C. Sun, L. Huang, and X. Qiu, “Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence,” In Proceedings of NAACL-HLT, 2019, pp. 380–385.
K. Zhang, L. Wu, G. Lv, M. Wang, E. Chen, and S. Ruan, “Making the relation matters: Relation of relation learning network for sentence semantic matching” In Proceedings of the AAAI conference on artificial intelligence, vol. 35 no. 16, pp. 14411–14419, 2021.
S. Gururangan, S. Swayamdipta, O. Levy, R. Schwartz, S. R. Bowman, and N. A. Smith, “Annotation artifacts in natural language inference data”, In NAACL-HLT, 2018, (2).
W. Liu, P. Zhou, Z. Zhao, Z. Wang, Q. Ju, H. Deng, and P. Wang, “K-bert: Enabling language representation with knowledge graph,” In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 03, pp. 2901–2908, 2020.
A. Karimi, L. Rossi, and A. Prati, “Improving BERT performance for aspect-based sentiment analysis,” arXiv:2010.11731, 2020.
A. P. Ben Veyseh, N. Nouri, F. Dernoncourt, Q. H. Tran, D. Dou, and T. H. Nguyen, “Improving aspect-based sentiment analysis with gated graph convolutional networks and syntax-based regulation”, In: Find. Assoc. Comput. Linguist. Find. ACL EMNLP 2020, pp 4543–4548, 2020.
B. Yu, and S. Zhang, “A novel weight-oriented graph convolutional network for aspect-based sentiment analysis,” J. Supercomput. 79, 947–972, 2023. https://doi.org/ 10.1007/s11227-022-04689-9.
Y. Tian, G. Chen, and Y. Song, “Enhancing aspect-level sentiment analysis with word dependencies”, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 3726–3739.
M. Chen, and X. Zhao, “A multi-scale fusion framework for bimodal speech emotion recognition”, in: Interspeech, 2020, pp. 374–378.
E.G. Moung, C.C. Wooi, M.M. Sufian, C.K. On, and J.A. Dargham, “Ensemble-based face expression recognition approach for image sentiment analysis”, Int. J. Electr. Comput. Eng. 12 (3) (2022) 2588.
C. Arumugam, and K. Nallaperumal, “EIAASG: Emotional Intensive Adaptive Aspect-Specific GCN for sentiment classification,” Knowl.-Based Syst. 260, 110149, 2023. https://doi.org/10.1016/j.knosys.2022.110149.
D. Priyasad, T. Fernando, S. Denman, S. Sridharan, and C. Fookes, “Attention driven fusion for multi-modal emotion recognition,” in: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing”, ICASSP, IEEE, 2020, pp. 3227–3231.
W. Li, W. Shao, S. Ji, and E. Cambria, “BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis”, Neurocomputing 467, 2022, 73–82.
K.L. Tan, C.P. Lee, K.S.M. Anbananthen, K.M. Lim, “RoBERTa-LSTM: A hybrid model for sentiment analysis with transformer and recurrent neural network”, IEEE Access 10, 2022, 21517–21525.
X. Hou, J. Huang, G. Wang, P. Qi, X. He, B. Zhou, “Selective attention based graph convolutional networks for aspect-level sentiment classification”, 83–93, 2021 arXiv:1910.10857.
K. Wang, W. Shen, Y. Yang, X. Quan, and R. Wang, “Relational graph attention network for aspect-based sentiment analysis”, 2020. arXiv preprint arXiv:2004.12362.
K. Sun, R. Zhang, S. Mensah, Y. Mao, and X. Liu, “Aspect-level sentiment analysis via convolution over dependency tree”, In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), 2019, pp. 5679-5688.
H. Wu, C. Huang, and S. Deng, “Improving aspect-based sentiment analysis with Knowledge-aware Dependency Graph Network,” Information Fusion, 92, 289-299, 2023.
C. Chen, Z. Teng, Z. Wang, and Y. Zhang, “Discrete opinion tree induction for aspect-based sentiment analysis,” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 2051-2064.
Kumar, Ravindra, Husanbir Singh Pannu, and Avleen Kaur Malhi. "Aspect-based sentiment analysis using deep networks and stochastic optimization." Neural Computing and Applications 32 (2020): 3221-3235.
C. H. Lin, and U. Nuha, “Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy”, Journal of Big Data, 10(1), 88, 2023.
K. Zeng, Z. Pan Y. Xu, and Y. Qu, “An ensemble learning strategy for eligibility criteria text classifcation for clinical trial recruitment: algorithm development and validation,” JMIR Med Inform. 2020;8(7): e17832
A. Mohan, A. M. Nair, B. Jayakumar, and S. Muraleedharan, “Sarcasm Detection Using Bidirectional Encoder Representations from Transformers and Graph Convolutional Networks”. Procedia Computer Science, 218, 93-102, 2023.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM, 63(11), pp.139-144, 2022.
C. Ledig et al. “Photo-realistic single image super-resolution using a generative adversarial network,” CVPR. 2017, 2(3):105– 114. https://doi.org/10.1109/CVPR.2017.19.
B. Lim et al. "Enhanced deep residual networks for single image super-resolution,” In: Proceedings of the IEEE Conference on computer vision and pattern recognition workshops. 2017
Demiray, Z. Bekir, Muhammed Sit, and Ibrahim Demir. "D-SRGAN: DEM super-resolution with generative adversarial networks." SN Computer Science 2, 2021 1-11.
J. Pennington, R. Socher, and C.D. “Manning, Glove: Global vectors for word representation”, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1532–1543
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